{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7b04b50",
   "metadata": {},
   "source": [
    "散点图\n",
    "散点图用于在水平轴和垂直轴上绘制数据点，它表示了因变量随自变量变化的趋势。通俗地讲，它反映的是一个变量受另一个变量的影响程度"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9367f769",
   "metadata": {},
   "source": [
    "pandas获取excel数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ebf945e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>月份</th>\n",
       "      <th>广告费用</th>\n",
       "      <th>销售收入</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01</td>\n",
       "      <td>24539</td>\n",
       "      <td>98984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-02</td>\n",
       "      <td>23760</td>\n",
       "      <td>88981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03</td>\n",
       "      <td>3107</td>\n",
       "      <td>13898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04</td>\n",
       "      <td>20950</td>\n",
       "      <td>71086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05</td>\n",
       "      <td>7659</td>\n",
       "      <td>25936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        月份   广告费用   销售收入\n",
       "0  2014-01  24539  98984\n",
       "1  2014-02  23760  88981\n",
       "2  2014-03   3107  13898\n",
       "3  2014-04  20950  71086\n",
       "4  2014-05   7659  25936"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "df = pd.read_excel(r'D:\\MyFile\\WareHouse\\Gitee\\daily_code\\期末\\4\\plot.xlsx', sheet_name='scatter')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38ff1107",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = df['广告费用'], df['销售收入']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ab4e39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '销售收入')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1VTp3yhZpqFA1ZHvvvXd0nzJPyu7FUoZIGS3V4akeSx3Q9TlR9kg1Xsom6fOl7t+6qXD/yCOPTOs8AWSSgDLQoUOHyPnnn5/282fMmFEkaxFP+5JlTuT77793+5VFEPWn0f0nnngi+hhlZJJlI3r16uW6F6fTAkDTwvXXvff8RC0AtC0+g5Oob5Cmv+vnKkvjNQvUcSfLAsWfD68rt5+bMkKx1DvJE98nyesFpX5N2n7zzTdHiqP3r07VXiPHWMrcqCWCMmYTJ05M+Hz1/zn99NPdz1JDx1TEZ5K8Y06UVSwukxT/u4/NJImXAZK//OUvrpFocf77v//bfU7mzp0bmTNnjvterR9+/PHHYp+ntgJ9+/aNzJs3L7pNnx1lI73P3wUXXFBiZg8oCZkkIMM020ZrSfmZZZRMSQW0sZLNoosteFVtkLJAqtsYMGCAXXHFFa6uQ1kkrT0WT7VH//znP0v1HlRYGzu9PlHWQsXcXiZH2ZRkmQytFaZ6n9h1uJR1ic1oJSs094qZVbcS+/j4mzJk8Vm54up8lGk76aSTXBsCFb6rLYAyXokWzVU2TQXoOvfKliTKHKrwXHU+/fv3d1PaY19HvwtlQ5TpUtsCzazT+y9ujbNY3np/3vnVOoCJsmlvv/12ocfF08+LnVUZz8sAiV6vdu3aSevNNN1fNUyTJ092Mx6VAVX2UVkiFbWrON5rYRH/uT7jjDNcRk5Zo2T0WqrJ89YsBNJBnyQgw3QR1EXPKyROh3fB9zOlPFmQdMABB7i2ABqW0wX6888/d4vsqghaFxctdKpi5SuvvNJd8GOHT7yZYieeeGKR44oPRvR68QGFgi8tIKvCW+8iHb8wrwIxDcd4fZ90zmIX9PVoar1mTqlAV/19vNeLpfveYqnxBfPeMaggu7iLqgLJ4qamez9TF2kVwuv3rPYECmA0VKmLvM7pX/7yFzfspJlVej0dt4q7NeNPC7Amo+HDN9980503DUvqvGooSgGF+igpkNB0e51nBWQKevW7UyCgdgsa3lWQpwBHAZZ+9wpEtN8rRvfOr4b4ktFMNa//lIrdPfq9q4dS/GcyWXCqPxbiZ9vpM6hFgXUeFLDr9fS+PJqFqfOp86eZiSr6178jDUXqXGpoTcXaCp70OH3GY39/3h8X+vxptpuCWLVGUCCWbDgWKFaJuSYAvqkLtAqNf//735fqdbRWmTd0FL++mXfTvkQdmzXEoGETLTWibtMaEjnzzDOjw24eFcQOGjTIDfEcdNBB0aElLWeiYRINVyRaE04LjHq0yKrer4YXi6MlSGKH+eSTTz6Jdhn3hrO09EUsdUxWUbnWUIulBWK9ZVbkueeei3Z8TrQ8it/hNi0Hk4w6m+sxTz75pCsE19Bc/DImej86Ryr4VmG2um6ri/jll1/uez0xdevWkFNsx24NacWvjaf3qSJm/Z60ELKKplXErMJldQNXgbx3fF4H9tgFfpPRch76zGi4NXZdQA3LJhpu0yQDz9tvv+3Oodbp0+fi/vvvj+7T5+rEE090hfBabDl22Zp4OlcqTteQrM537PCcfsexy41o+ZxLL73UDdWpS3wsfZ60Fp/f5ViAeAX6T/FhFIBUqGBaf+FqWnVZ0l/wyjYoK+BlSzzKQCiToOyEOktr2nUyGjbR0IhX3Kqsg4ZzNL09fihO2Rr9Fa+Cb1HWRBkh/bWfbGilPCibpnOhzE68NWvWuCyLhtuOO+64pK+h86TzpXNXEhUKl1Sk7VGvKL/9i4Iy6UC/y/i+WcpCKpMT27Vc50ptCFSQLZoUoMzV4Ycf7rJqV111VaHsnDJ5yk7FD2sWR5eo2OFnDfnFNr1U1k0tFPS7VWF8os8AkC6CJAA5Tf+LU6Cii346vasA5C+CJAAAgASY3QYAAJAAQRIAAEACBEkAAAAJ0CepFDOL1OBOxaCpNP4DAADZo1JszdLdd999i+2NJgRJaVKAFL+mEAAACAet8aempsUhSEqT10NEJzmb/WEAAIB/6qmmJEd8L7BECJLS5A2xKUAiSAIAIFz8lMpQuA0AAJAAQRIAAEACBEkAAAAJECQBAAAkQJAEAACQAEESAABAkIKktWvXWosWLWzFihXRbQsXLrROnTpZvXr17KabbnJdMT0zZsywNm3aWMOGDW3EiBGFXmvSpEnWrFkz1z1zwoQJhfaNGjXKGjVqZC1btrTp06cX2nfbbbe5n3X44Yfb/Pnzy+y9AgCA8KmQrQDptNNOKxQg7dy5004//XTr2LGjffrpp7Zo0SJ77rnn3L41a9bYGWecYf3797dZs2bZ888/b++88040sBowYIANHTrUpk6danfccYctWbLE7dP9G2+80Z566ikbP368DRw40NatW+f2Pfnkk+72yiuv2D333GP9+vWzXbt2ZeN0AACAAMpKkKSA5Lzzziu07fXXX7eNGze6LFGrVq3s3nvvtWeeecbtU1CkLJECodatW7tAyNs3ZswY6969uwuA2rVrZ4MGDbJx48a5faNHj7YLL7zQevfubV27dnVfJ0+eHN2nAOr44493AdjBBx9s7733XrmfCwAAEExZCZKefvppGzx4cKFt8+bNsy5duliNGjXcfQ2BKZvk7VMg5HXH7Ny5s82ePTu6r0ePHtHX8bNPw3gLFixI+rxElOlSK/PYGwAgs37eE7FZy9bZlLkr3VfdB7IlK8uSqBYpnoKO2O0KiCpWrGgbNmxw+9q2bRvdp2VAtMBsouf52bdlyxbbs2dPkX1Lly5NeszDhw+3YcOGlep9AwCSe2Phdzbs1UX23cYd0W1N6lSzO09vaycd1iSrx4b8FJjZbZUqVbKqVasW2latWjXbtm1bkX3e9kTP87NP2yXZ8xIZMmSIGw70blrYFgCQuQDpyvGfFQqQZPXGHW679gN5GyTVr1/fFWjH2rx5s1WpUqXIPm97ouf52Ve9enV3S/a8RBRQeYvZsqgtAGSOhtSUQUo0sOZt036G3pC3QZKm/mvmmmf58uWuDkiBTvy+OXPmWNOmTRM+z+++o446Kuk+AED5+Xj5+iIZpFgKjbRfjwPyMkj61a9+5WqIxo4d6+5rdluvXr1cXZJmn82cOdOmTZtmu3fvtgceeMBOPPFE97i+ffvaxIkTXSG2ao0effTR6L6zzz7bHn/8cVu5cqV9//33bkZc7L7777/f/UzVIqnXkrcPAFB+fti8I6OPA0JduJ2I6oQ0nV+9kNRIskKFCvbuu++6fWogOXLkSDvllFOsVq1aVrdu3WgPpSOOOMKuueYalxlSXZFaBFx11VVun/ouvfjii26b9OzZ08466yz3/eWXX25Tpkyx/fbbz2Ws1EJAPZoAIAw09KTMigKHffaqZp1b1LeKFX6ZARw2Ov5MPg659xnJloJIbFvrAFi9erWbiq92AA0aNCi0T0Nwixcvdr2NFCzFUrsAZYy6detWpLbok08+sa1bt7p9XhsB0Qw3ZahUb6QWAKlQBqpOnTquiJv6JADlKddmgeliftz9012RdqILkv6v3bhONfvg5h5c5PP0M5JJqVy/AxckhQVBEoBszgKL/x+3FzqMPr9Dxi+C5ZGR8N6XRMrpfeWqbHxGcvX6HZjhNgBA6WaB6SKo/Se0bZyxIKa8MhJ6LV2843+WMkhkP4L9GcllBEkAkIOzwI5pVbhcIZMZCa93UaYzEnotXbypownPZyTX66kIkgAgJMpzFli2MhJ6rSBfvIMuF2YKBqmeKjAtAAAAwZkFRu+icAr7TME3AtZ5nSAJAEJCQw76izpZ3kbbtV+PK61cyEjko/L8jORD53WCJAAICQ1FachB4i+C3n3tz8TwV9gzEvmqPD8jmRbE7CVBEgCEiDcLTLO+Yul+Jgupw5yRyHfl9RnJtCBmLyncBoCQKY9ZYF5GQnUgBUl6FwU1I4FwzhTcJ4DZS4IkAAih8pgF5rd3UVCma6cr7MefKzMFO/8ne1lS5/XyzF4SJAEA0s5IBGm6djrCfvy5pGIAs5csS5ImliUBkO/CvvxF2I8/V71RxoEra7eVA4IkAPk8ROQtSptsNlLQF6UN+/Hnup/L8PPN2m0AkMOCMEQU9uUvwn78ua5iQOqpaAEAACESlI7EQZyunU/Hj/JBkAQAIRGkjsRBnK6dT8eP8kGQBAAhEaSOxGFvNhn240f5IEgCgJAI0hBR0Je/UDZt1rJ1NmXuSvc1PrsW9ONHMFC4DQAhEbQhIr/NJoNa2B7U40dw0AIgTbQAAJCtaesldSQu72nr2W5HUNreR0E6fpQ9WgAAQA4KYkfiIE3XLqmwXWdF+9VBPPYcBeX4ETzUJAFAiIR1hfd8K2xHbiCTBAAhE8YV3vOtsB25gSAJAEKIIaLgF7Yj/AiSAAA5wet9VFJhez72PqI4PT0ESQCAnBDUwvZsC8Jaf2FF4TYAIJBKagiZCIXtwVzrL6zIJAEAAjcctGHrTrv7tS/Syn5Q2F66lgj4fwRJAJAl1IkkHw5KxMt++MkIUdieWkuEfD9XyRAkAUAWUCdSfIdsK4PsR74FpbREKD2CJAAISGCQSqYkTJIFJ8UNB2U6+5GPQSktEUqPIAkAylG+1YkUF5zUqV6lxCG2TGQ/yiooDXpmipYIpUeQBADlKJ/qREoKTi45tnnar+03+1FWQWkYMlO0RCg9WgAAQDnKlzqRkoITmTx3ZcqvW/CfYMRv9qMs1nML07R6WiKUDpkkAChH+VIn4ic4Wb91t9WvWcU2bN3lqy4pnexHpoPSMA6X0hIhfWSSACALdSIFGcqUBJXfoKP9/nV8F26nk/3IdFBaFpmp8uC1ROjdvqn7SoDkD5kkAChH+VIn4jfomL54TdJ9lx3fwnoc0qhU2Y9MFy/ny3ApfkEmCQDKWT7UiSjoaFy7aqle4+/zv3OvU5rshxeUSvyz0wlK82W4FL8gkwQAWZDrdSJ6H/07H2Ajp/0r7dfI1Cw/LyiNn43WOI3ZaEyrzy8ESQCQJbm+dMYB9WuU+jUyNWyVqaA0X4ZL8QuCJABAxmkavBaoLa1MDltlKijNZGYKwUaQBADI2npsyQR92CrXh0vxC4IkAEDGpLMeW1iHrXJ9uBTMbgMAZFBJfYQ89WtWtifO7+BuuTzLD+FGJgkAkDF+C62HnnZoNAhi2ApBRZAEAMgYv4XWjWv//+MYtkJQMdwGAMiYfFl2BfmBIAkAkDGZ7nAdxML0WcvW2ZS5K91X3UfuYrgNAAJEF92w1+fkah8htTaIf09NQv6eULyCSCRCGJyGTZs2WZ06dWzjxo1Wu3btbB8OgByQaxfhXAj4Sur95L0bZuPl5vWbIClNBElA7ivPizwX4WB/Do67f3rS1gZe48sPbu4R2iAwn2xK4frNcBsAZDmrU1wDRm/brZMXWI9DGlmVSpSSBq33UySDi/EiWPjXBgBJsjrxF0at/K7t2l/eDRjXb91tXYa/nfGfjcz1fsrUYrwIDoIkAEgxq6P9mZzV5Pfiun7rrjIJ0pCZ3k+ZXIwXwUCQBABpDq1kSqoX10wHaSgevZ/yF0ESAGR5aKWki3BZB2nI795PSI4gCQCyPLQSexH2i/qX7PR+YjHe/MLsNgBIkNVRkXakmOnemR5a8S7CmsWmIu10g7Rc6k0UNPodsRhvfiFIAoAEWR0VSOvSFynHoRVdhDXNX7PYVKRtKQZpudaMMohYjDe/MNwGAAEaWlEfpHv7HOaCoVTqX8q7bQGQD+i4nSY6bgO5L5tDV6lkhcqiIzTDdshVdNwGgJAPraRS/5LpjtAM2wG/IEgCgJAHaZlsW5BsDTlv2I6ZXImRectNBEkAEHKZaltQUrdxXfK1XxkuAoD/R+Ytd1G4DQAhl6mO0NnoNh52FMznNoIkAAi5THWEDtJCrspqzVq2zqbMXem+BnEZlmys84fyFcggacyYMbb//vtbjRo17Ne//rX9+9//dtsXLlxonTp1snr16tlNN91ksRPzZsyYYW3atLGGDRvaiBEjCr3epEmTrFmzZrbvvvvahAkTCu0bNWqUNWrUyFq2bGnTp08vp3cIAMFrWxCUhVyVfdFsvf5Pf2jXTJzrvup+0LIyZN5yX+CCpGXLltkf/vAHmzJlii1evNhatWplF110ke3cudNOP/1069ixo3366ae2aNEie+6559xz1qxZY2eccYb179/fZs2aZc8//7y988470cBqwIABNnToUJs6dardcccdtmTJErdP92+88UZ76qmnbPz48TZw4EBbt25dVt8/AKRLgZCm+U+4rIs90q+9+6r7qiHyk5UJwkKuYRq+ClLmDXkSJM2ZM8e6dOliHTp0sAMOOMAuueQS+/LLL+311193PQ2UJVLgdO+999ozzzzjnqOgSFkiBUKtW7d2gZC3T1mp7t27uwCoXbt2NmjQIBs3bpzbN3r0aLvwwgutd+/e1rVrV/d18uTJWX3/AJCJGXG92zd1X99atNp3VibbC7mGbfgqKJk35FGQ1LZtWzfsNXfuXBcUPf7443bCCSfYvHnzXPCkITg5/PDDXTZJtE+BUEHBL/9wO3fubLNnz47u69GjR/T1/e6Lp0yWGlDF3gAgyNLJymSz23jYhq+CkHlDnrUAUJB09tln25FHHunut2jRwj766CO777773PceBUQVK1a0DRs2uIBFz/Oog+aqVavc99oX+zy/++INHz7chg0bVgbvGAAy36NH0p3On62FXMM2fJXNdf6Qp0HSxx9/bK+++qp9+OGHdsghh9gDDzxgp5xyisv4VK1atdBjq1WrZtu2bbNKlSoV2udtl3T3xRsyZIhdf/310fsKsFRcDgBB7NHTr9MBperCnY1u42EcvvIyb/G/A2Xe6JMUfoELkjT7rF+/fnb00Ue7+/fcc4+rHVJ2SUXYsTZv3mxVqlSx+vXru+Lt+O2S7r54CqbigzQAyKbiumOPnLY0VFmZ2OErHX+kmDXogjZ8la3MG/KwJmnPnj32ww8/FApcvGyRZq55li9f7uqEFOioLUDsPhV/N23a1H2f7j4A4RWGHjvlUeQctqxMtgvHM1kwH8RjRA4ESccff7y99NJLNnLkSHvhhRfszDPPtMaNG9vgwYPdENfYsWPd4zS7rVevXq4uSdP/Z86cadOmTbPdu3e7IboTTzzRPa5v3742ceJEW7BggW3ZssUeffTR6D5lp1QYvnLlSvv+++/djDhvH4BwCkuPnbIuci5JUIuKs1k4DgR+uE1BzRdffGEPP/ywfffdd3bYYYe5afmVK1d20/nVC0mNJCtUqGDvvvuue44aSCqoUu1SrVq1rG7dutEeSkcccYRdc801dtRRR7maI7UIuOqqq9w+9V168cUX3Tbp2bOnnXXWWVl89wBKo7wXZ83moqapDJOFraiY4SsERUEktm11CKxevdpN01c7gAYNChcVaghODSiVjVKwFEvtApQx6tatW5G6o08++cS2bt3q9nltBEqirFadOnVcmwLNigOQXQpYlDFKll3x6lnUXDETF9tsL2qqYURlyUpyXa/WNvGTb1h8FUjj+h26ICkoCJKAYPEbNKgLdWlnbSXLWHmhV3kMC3lBYUlFzgoKpbyyMqXNrmUzO4f8sCmF63fghtsAIMg9dkoqmC6u/1A2e/SUx3T+0mbXsp2dAwJfuA0AQe6xE6Su0EEqci7tmmthWrMN+YNMEoCcUF49doLWFToIRc6lza4FJTsHxCOTBCAnlFePnSB2hc52j57SZteClJ0DYhEkAcgZ5TH8xKKmmc+uBS07B3gYbgOQU8p6+IlFTTOfXQtidg4QMkkAck5ZDz8FqWA6CEqbXSM7h6AikwQAIS2YDorSZtfIziGoaCaZJppJAkBh9ElCGNBxuxwQJAFAUXTcRtDRcRsAkNV6sGw9H8gkCrcBAAASIEgCAABIgCAJAAAgAYIkAACABAiSAAAAEiBIAgAASIAgCQAAIAH6JAFADqAJI5B5BEkAEHIs5wGUDYbbACDkAZIWho0NkGT1xh1uu/YDSA9BEgCE1K6f9titkxdaogU4vW3KMGkoDkDqCJIAIISUIeoyfJqt37or6WMUGinDpFolAKmjJgkAQjrE5jc/pGJuAKkjkwQAIaKhMw2hpTKAptluAFJHJgkAQkRDZ/FF2smoAUDjOr+0AwCQOjJJABAiqQ6dqQ0A/ZKA9BAkAUCI+B06a1Czio0+vwN9koBSYLgNAEJEQ2dqFKk+SMnqkurXrGyzhvS0KpX4OxgoDf4FAUCIaOhMQ2iSbBCtT/umNvurDfRHAkqpIBKJ8K8oDZs2bbI6derYxo0brXbt2tk+HAB5JtFSJCo9io2LWJoEKN31myApTQRJAIKyqO1bi1bbszNXFNnvZZqoTQLSu34z3AYAIR56U43S6wtXJ9zP0iRA6RAkAUAO901iaRIgfQRJAJAHfZNYmgRIHUESAORB3ySWJgFSR5AEADnQNylZOwBt136WJgFSR5AEADnaN8m7z9IkQHoIkgAg5DS9X9P8tZhtLN1n+j+QPpYlAYAcoEDohLaN3Sw2FWmrBklDbGSQgPQRJAFAjlBAdEyrBtk+DCBnECQBABJ28iYjhXxHkAQgbVxM82NNONaAQ74iSAKQFi6muRds6nd65fjPosuZeFZv3OG2UwSOfEOQBCBlXEwzF9wEJdjUe9FxJFrhTdv0rrRfxeFkC5EvCJIApISLaeaCmyAFm6msAUdxOPIFfZIApIQFVf8/uIk/D15wo/2lDTZF+/W48sAacEBRBEkAUpLvF9NMBTdBCzZZAw4oiiAJQN5cTBW4zFq2zqbMXem+ppOlyVRwE7RgkzXggKKoSQKQ1sVUQ0uRJBfTxgG8mGaqQDpTwU3Qgk1vDTgNF+p3GPu7ZQ045CsySQByfkHVTNQQZTq4KY/MTaqZM9aAAwojkwQgZd7FND4z0ziAfZIUGNz1yucZm42XqUxaWWdu0s2csQYc8P8KIpFI+UydyDGbNm2yOnXq2MaNG6127drZPhwgr5sgFueRaf+ykdOWlvi4CZd18T213ctMWZLgJpWsS1n0SUrWWiCd4wPy+fpNJglAzi6oqmDBT4CUaoF0JjNpmc7c0McKyByCJAA5yQsW/Eq1QDqTwU0mg02aQgKZQ5AE5LkwDJmVRbAQK90C6SBm0oLWWgAIM4IkII8FZd2wspBKEBC02XilEbTWAkCY0QIAyFOZnBYfRH6DgOt6tQ59QBiLppBA5hAkAXkoaOuGZSNYEO0f1KO15ZIw9rECgoogCchDQVs3LBvBQkEOBws0hQQyg5okIA/lS3FvmJpeZhpNIYHSI0gC8lA+Fffmc7AQxNl3QN4Otw0ePNjWrl2byZcEUAbyrbjXCxZ6t2/qvuZDgAQgQEHSqFGj7LHHHnM3AMFGcW84pbpgLYAArN322muvWZ8+fey0006zSZMmWYUKuV8PztptyAW53Ccp1/C7Asr/+l2qaOann36ye++9184880zr27ev/fWvf81ogHTzzTfb6aefHr2/cOFC69Spk9WrV89uuukmi43vZsyYYW3atLGGDRvaiBEjCr2OArdmzZrZvvvuaxMmTCiSAWvUqJG1bNnSpk+fnrFjB8JAF9cPbu7hFnd9pF9791X3uegGS673tAKCKq2IZvPmzW5YrVWrVvbkk0/a2LFjXfBRqVLm6sDnz59vjz/+uD3yyCPu/s6dO13A1LFjR/v0009t0aJF9txzz7l9a9assTPOOMP69+9vs2bNsueff97eeeedaGA1YMAAGzp0qE2dOtXuuOMOW7Jkidun+zfeeKM99dRTNn78eBs4cKCtW7cuY+8BCIMg1+swvJQfPa2AnBhuW79+vQ0fPtwFFQqQfv/739v5559vlStXzuhB7dmzx7p27Wq/+c1v7A9/+IPb9vLLL9sll1xi3377rdWoUcPmzZtnV199tX3wwQf28MMPu2BNgVNBQYFNmTLFXnzxRRf4XHvttbZ48WJ744033Oso6FJQdc8997gMWOPGje2JJ55w+6677jo79NBDXbBUEobbgLLF8NIvFBz2f/rDEh+nLCAz2YAsDbdNmzbNDjroIBeQKEAaPXq0XXzxxRkPkERBy4IFC6x58+b2yiuv2K5du1xQ1KVLFxcgyeGHH+6CItG+7t27uwBJOnfubLNnz47u69GjR/S1/e6Lp0yWTmzsDUDZYHgp/3paAUHkK0hSrU7v3r1djdAPP/xg5557rhv6OvbYY12gkUlbtmyxO++809UIffXVVzZy5Eg77rjjXFDSokWL6OMUEFWsWNE2bNhQZJ8iw1WrVrnv090XTxk0RZ7ebf/998/o+wbwC4aX8renFRC6IGnr1q2u1ufuu+92xdIqmlawtHz5cjv++OPtmGOOsSFDhhQqoi6Nl156yf1M1RQNGzbM3nrrLVcD9eyzz1rVqlULPbZatWq2bds2VwsVu8/bLunui6f3qNScd/vmm28y8n6BIAhS7U8+LJmSinzraQUESYmV1jVr1rS//e1vLpsTv/2+++6zc845x90+//xzV7yt7aWhmiMNq2mWmjvASpXc0JrqilRLFEvBU5UqVax+/fqF9nnbJd198RRMxQdpQC4IWu0Pw0uJe1ppmFEBUWz4Sk8rIADDbfEBUqwOHTq4GWVff/21nXLKKa5+qDT2228/2759e6FtGnZTLZR+jkeZLNUJKdBRW4DYfXPmzLGmTZu679PdB+SDINb+BH14KRtZNxasBULcTFJWr17tAiYFVOqXlC5NwVc90v333++aU2r4TcN7S5cuddP/tV0F45dddpn7ma+++qpbCkU1Qvq+W7durh3AgQceaH/6059czZRqpxQMqf5IQ4SakXfDDTe4ovArrrjCPvnkE5ex0utr9pt6PpWE2W0IO13cj7t/etKhrYL/XITVN6k8sxTecSlQiwTouIKQddO5ycc16IBMSuX6nbEgSTT1Xs0kYxtApmPmzJmuf5ECnCZNmrgskl5TQY3qo6pXr+5+zrvvvmtt27aNzojT2nG1atWyunXruqBITSLltttuswcffNDVHLVu3dref/999xp66xdccIEbTpSePXu6n+HNkisOQRLCLshTy70MlyUZXspG9sQ7pvj/YWbzmAAEKEjSUFqymh3RS/kJMEpD2SNN01fdUoMGhf/HrSE41S4pW6RgKZbaBaxcudJlmuLfgzJJKhbXPr/HT5CEsNNw0TUT55b4OHXiVqPJ8pbtrE0Ysm4AUpfK9btSKgHSXnvt5eqAklFjxzFjxtif//xnKytq/njqqacm3KfhtNgp/bGUcfKyTvFUmwTkm6DX/igQOqFt40AML6Uy446GjkDu8B0kKfuixpHK1Kj+J9EsNq2DpkAKQHimlpdU+5PNqeXekinZ5ncm3eqN290wZraDOgCZkdJiaz///LMralaPoL333tsNT/Xr188tH6JFZNV0UmuuAQg+ppb75zebdvdrX9j6rbvyehkVIJekVJOkwEh9hfSUFStWuKVKnn76addfSLPSVPSsWqF8QE0SckWQan+CqqQZd8lQ1A3keE2SFptVs0h13PaKmvVVL67Gj999950belPNUlms4wYgf2p/wph1K44ep8crCNU55pwCOdZMUsGQgqBf/epXbsmOuXPnuqn1Wsvtxx9/dH2MVKekKfvapowSgHA1MPRqfzSLTV+5mPtv6Fi/ZvF/HObbMipAXg63aYr82LFj7Y9//KPrXTRx4kRr3rx5ocf813/9lxuKU31SrmO4DeWF4bBgiW/ouHrTDrvuL8FtpQAg/eu3r2VJRNmkQYMGuc7XmoI/fPjwIo/RWm7aX1ybAADhXjYk38Vn3RrXDnYrBQBlPLtNS3jEL+6qNdNuvfXWIo/t3LkzC8ECGcpYKIMUyYFal1xeTiMMrRQAlGGQpIBIS3ok8tBDD7kgSjTUlqh/EoD8bWCY68OFtFIA8jxI0tpnyfzjH/+wk08+OWk3awBl28DQ7+OyIdl6Z95wYa5MjfeKuuODwcY5FAwC+ch3M0nNYlPWKH4oTTPbtI8gCcivZUPyabjQD1opAHkcJHXs2NFllOKDpI8//that25dFscG5LWw17rkynBhGJdRAVDOQVKzZs3cLV7Pnj0zdCgAcqnWpSyHC3O5EBxACIOkyZMnuzXaYguzP/zwQ/v+++9dE0kApRd/8dfwTVhrXcpquDDXC8EBhDBIeuqpp+zII4+03/72t64G6bTTTrMrr7zSLU3y2GOP2Y4dO9xyJfpaXKE3gNQv/h/c3CN0mZOyGC7Ml0JwAMHgu5mk1mWrVauWzZ8/393XV22rUaOGjRgxwj7//HNbuHChaygJILNNI99atDp0y4Z4w4USf7TpDBeWVAgu2p/pJVsA5C/fQZJ4QZFUrFjxlxeo8MtLaMkS3erWrVsWxwnkrExc/Mt6bbdMr3em+6lmfVIpBAeAchtu++c//+m+btiwwX7++Wdbv3697dmzxzZv3lzksRpyA1B+s8CCXqOTqanxudA3CkCOZZL+93//19UeqdaoZcuWri9Sw4YN7ZtvvrFzzjmnfI4SyGGlufiHZW23+PXO0hkuDHvfKAA5GCSde+65Nnv2bLcsiTJJhx56qMsiqR2Aum0DKJ10L/75VqPjFYInC6+0vUmA+0YByMEgSc0jK1VKPCrH0BqQvYt/vtXoZLoQHABKHSQpe3TxxRfbxo0bCYqAAF3887FGJ5OF4ABQ6sLt7777ztatW2czZ8607t2727Jly6xz585u++DBg92abZFIxC655BL3VUGV6pc0PAeg7BZIzdcaHdZIAxCYIElB0CuvvGJLliyxQYMGuTYAZ511lg0dOtTVJtWpU8e++OIL971urVq1irYFAFB2F/+wr+1WGqyRBqA8FESU/knBM888YzfddJP99a9/tV69elm+2rRpkwsQNQxZu3btbB8O8pQ3u82SrO3GEBQApH/99h0k/fTTT9EC7q+++soOOOCAvK5RIkhCUAS9TxIAhPX67auZpF7oqKOOsrvuusv69OljQ4YMcbPe4oMk3e/WrZtdcMEFpXsHAHyjRgcAyoav4iFFXA899JA9+OCDNm3aNLduW5cuXeytt96yo48+2t58801XzL333nu7xpO7d+8uo8MFUFbNGstSUJdNAYBSZ5K0/MhJJ51kPXv2dGkqDbVdfvnlNmrUKPf1sccesyuuuMIFRyre1tCcCrwBgOFAADmdSapfv741aNDAmjRpYgcffHDSxykweuCBB6x69eqZPEYAIRWWZVMAIO0gSRkiZZNWrFhhX3/9dZH9+VzADWRCLg5H5duyKQDydLhNE+A0pDZs2DB7/fXXbdasWda1a1fXWDL2q+ef//xnWR4zkFNydTgqlWVT6HkEINQ1SW+88Ya9++671qZNGxcoaXZbfNNIBVPbt28vq2MFcnY4Kj6X4g1HhbnPUT4umwIgD4fb1EdgypQp9vbbb1vFihVdbVL79u1dEffSpUvtiCOOcMuRtGvXzo455piyP2ogB+T6cFS+LpsCIHektH7InXfe6b5qaG3Xrl22atUq+/LLL933//M//+OWLQGQ+eGoMPKWTUlWsajtTXJ02RQAeTTcdu6559phhx0W7bitWWxqKrl69Wo3vPb++++7RW2ffPJJq1u3rg0fPrysjxsIvVwfjlKvJtVVadiwIMmyKdoftJ5OAJBSkNS3b1+7++67bdu2bfb555+7Pkg33HCDffDBBy5Q6tevX3S5ErUKAFCyfBiOUj2V6qriC9Mb50BhOoDc5ytIOuecc9xt6NCh9utf/9pGjhzpvu7cudO++eYbtxQJgPSGo1SknajqqOA/wUTYh6NYNgVAWPle4NYze/Zs69ixo+U7FrhFJme3WZLhqDDPbgOAsF+/UyrcFgIkIPPDUcoYxdJ9AiQACMFwG4Cyw3AUAAQTQRIQAAqI6DoNAMGS8nAbAABAPiBIAgAASIAgCQAAIAGCJAAAgAQo3AZ80CKzzD4DgPxCkAT4aPgYv6yGOmWzrAYA5DaG2wAfHbFjAyTRUiLarv0AgNxEkAQUM8SmDFKidXsi/7ndOnmB7fppTxaODgBQ1giSgCRUgxSfQYq3futu6zL8bTJKAJCDCJKAJFSk7cf6rbsYegOAHESQBCShWWyp0NCchugAALmBIAlIQtP8NYvNz0R/hUYamvvw3+vK4cgAAOWBIAlIQn2QNM0/FVc/z7AbAOQKgiSgGOqDNPr8Dla/ZmVfj/9x++6M1ydpCG/WsnU2Ze5K95UhPQAoHzSTBHwESj0OaeRmsalI22990gltG5e6KzeNLAEge8gkAT5UqVTB7u1zWEr1SWohUBo0sgSA7CJIAlIceqtbvXJGWwik08hSmE0HAGWLIAlIMVAadV6HMmkhkEojS7/ZKuqZACB91CQBKerSqoGrC9KwV6KQQ0NyjWpXtT2RiAtOFCypnUAq9Ul+s1DFPY56JgAoHTJJQClaA8SHPbqvwGnHT3tswJiP7JqJc63/0x/acfdPT6mGyG8WasXarQm3U88EAKVHkASkwBu+2vnTHru210HWqHbhYKZOjV/qlX7ctrtUwYnfRpYjp/2ryGtSzwQAmcFwG+BTouGrxrWr2nW9WlvzhjWtYc2qdsOL88yscIAkCkcKUmgN4GWrFFgVJ9FrplLPdEyrBj7eOQDkJzJJgA/Jhq++37TTHp72L6taqYJVqFBgqzeVvtjao7ohZauKk+g1/dYzTVu02tfjACBfBTpIOumkk+y5555z38+YMcPatGljDRs2tBEjRhR63KRJk6xZs2a277772oQJEwrtGzVqlDVq1Mhatmxp06dPL7Tvtttus3r16tnhhx9u8+fPL4d3hDDyO3xVXICUbmuA5g1rpPyafuuZnpm5IuFQHbPhACDgw23PP/+8TZ061fr162dr1qyxM844w2644Qbr37+/23bkkUda9+7dbeHChTZgwAAXDB199NF21llnWYcOHezggw92z7/xxhtt4sSJtvfee9v5559vn3zyiTVo0MCefPJJd3vllVdsw4YN7jXnzp1rVapUyfZbRxYpKFBWRkGHNyvN7/DV+i07M94awO9j9Tjv2BWsaRmV9VuLDvvFix2qYzYcAIQgSFq/fr0LiBToeAGTskRDhw61goICu+OOO+yZZ55xQdKYMWPc14EDB7rHDho0yMaNG2f33HOPjR492i688ELr3bu326evkydPdo/VPgVQxx9/vNs3duxYe++996xXr15ZfOfIpmRBwsmHNfb1/Po1q5TYGqBxnV8CL7+8Au6SXnPD1l1uBl1xwVwi3lDdxu273HBi/M/wCs7VRJNACUC+CeRwmwKkPn36WJcuXdz9efPmuUBIAZJ07tzZZs+eHd3Xo0eP6HP97ItEIrZgwYKkz0tk586dtmnTpkI35I7ipsw/O3OFr9doXKd6sa0BRPtT6ZdUUrsBOeOIJnb1C0WP3a/VG7czGw4AwhAkvfPOO/b222/bAw88EN2mgKRFixbR+7Vr17ZVq1alvW/Lli22Z8+epM9LZPjw4VanTp3obf/998/gu0aQa44UjBQX12iXsj3K+nhLlyi7E0v3083GFPeao8470l6Z913CY/dLi/Zmors3AOSaQA237dixwy6//HI3FLbXXntFt1eqVMmqVq0avV+tWjXbtm1b2vu0XZI9L5EhQ4bY9ddfH72vAIxAKTf4qTmKRAo3iywuQ6SgRnU+8bVNqWSQ4iV7zZKOvTjeUF39Wv//76Cs1qIDgDAKVJB09913W6dOnezUU08ttL1+/fqueNuzefPmaIF1OvuqV6/ubtqnDFL88xJRQBUbVCF3+L34X3psc/vHwtWF+yQlKWxWQJTpHkSJXjPdwCU2uKtTvUqZr0UHAGEUqCDphRdecIFL3bp13X1ldv7617+677t27Rp93Jw5c6xp06buewVVs2bNsksvvTTpvp49exbZd9RRR7l9rVq1iu7zCsWRX/xe/Hu1bWy3nto2oxmi8jr2+NluscGdhhszXXAOALkgUEHS+++/bz/99FP0vmafqXj7oosuckNb06ZNs27durl6pRNPPNE9pm/fvnbsscfaNddc42qMHn30UTfVX84++2y74oor7OKLL3ZDbJoR98gjj0T33X///a61wOrVq12vJc1uQ/7xO4PMC4iC1KXa77HPuKm7zf5qQ8LgLra7t5/hRADIF4Eq3N5vv/2sefPm0VutWrVc80jdRo4caaeccoprDLlkyRK7/fbb3XOOOOIIFyApM6QsUcWKFe2qq65y+04//XSXRWrdurULoNRbSX2URLVP++yzj/uZ7dq1c4FYx44ds/r+kR1+ZpAFNUjwe+xVKlVwwV3v9k3d1/j3UhYF5wAQdgURzYcPieXLl9vixYtdbyMFULEWLVpkK1eudJmm+NoiNZDcunWr2+e1ERDNcJs5c6arNVILgFSocFuz3DZu3Bita0K4hbmZYqaOPVEzzSAGhwCQrlSu36EKkoKEICn3KED4cNk6m/XvtS4Po4xLl5ZFsy5BRYADAJm9fgeqJgkIUibmb599G4oskido9VIAEHaBqkkCgtZtW9vjF4EFAOQHgiTktZK6bQtLcgBAfiJIQl7z022bJTkAID9Rk4S85rdjdfzjglAkHYRjAIBcRpCEvOa3Y3Xs44LQKiAIxwAAuY7hNuQ1r2N1svyLtjeJWZIjCEXeQTgGAMgHBEnIa6l02w5CkXcQjgEA8gVBEvKe3yU5glDkHYRjAIB8QU0S8J9A6YS2jYsthE63yDuTgnAMAJAvCJIAnx2r0ynyzvSMtEwcAwDAH4IkIMUibxVIJ6r4KfjPEJ1X5F0WM9JKewwAAP+oSUIgKMMya9k6mzJ3pfsaxMLjVIq8y2pGWmmOAQCQmoJIJBK8q1GOrSKM3Or5k8rxKtj7cNk6u/qFz+zH7bsTvp6X/fng5h6+g5uwnTMACOP1myApTQRJmeFlWOI/hF6oEDu7LEj81BYlCmSKM+GyLsXWRKVzDACA9K/f1CQha0rq+aPLvfZr1lnQLv4lFXknC/4yOSOtpGMAAJQONUnImlzt+VNc8FccZqQBQLCQSULW5GrPn5KCv3jMSAOAYCJIQtaUV8+fsqzdSfTaqQR1zEgDgOAiSELWlNTzR+rWqGx79kRcMJJOEFGWs8CSvXa/Tvv7fg1lkJiRBgDBxOy2NDG7LTO8Amcp7oOYTmBTljPn/jF/lV31wpwi2/Xakf8Edxu37U4e/FWvbKMGdLAuLRuQQQKAgF6/KdxGIBeXjZdq00Vlnu565fOkM+dEWaB0mlb+Y/53NmhC0QDJe+3YkCdRw0fd7uvbzo49sCEBEgAEGEESAhEoqZHi8wOPdhkWy0Bg89j0L231pp0ZnzmnIO2qFz6z4g5Bu37cttuu7XVQkeBP94Pa+wkAUBg1SQgEZVQqFBQk7UodH9iU1KNo5LSlvn5uKkXW3tR+v5o3rOGCPxo+AkA4ESQhq2Jnh/3r+82lDmxSDWRSmTmX6tR+vTYNHwEgvAiSkDWpLtvhJ7BJJZBpkmJvolSyTqm+NgAgeAiSkBXpLNvhp+liKoFMqr2JUsk60fcIAMKPwm2EYtkOv00X/QYy1/VqnXLxtNfXqbjQR4f2+HlHUpgNADmAIAnlLtXanlRmhfkJZLR/UI/WlioFZwrSJNnrP9a/g51y+L4pvzYAIHgYbkO58zskNqj7gda6Ua2UZoV5gYyG8rzGjsVlo1JZskSPrVO9il18bHN7ee4qW791V8a7eAMAgoMgCeXO75CYmi2mMzPMa1AZXxQevwRIKkuWJHps/ZqVrU/7ptarbWOm9gNADiJIQuDWbPNToF0SBTkntG2cNEuUrHDc6+wdO7SX7LEbtu62Z2eusE4ESACQk6hJQrkrrrYn2ZDYrGXrbMrcle6r36VEvB5Fvds3dV9jXy9Z4Xh8Z+9UHgsAyC1kkpAVZTEklqnC8fglS/w+lqaRAJBbCJKQNZkcEiuLwvFUei6l8lgAQDgQJCGrki3bUdIwl8Io7VeQlWo9kN/C8VSaR6byWABAOFCThEBKdUgsFSX1UiqIWVYklccCAHILQRICIb44e/XG7WU2zJVK4XiqReYAgNzBcBuyLnEPoiplOszlt3A81ccCAHJHQSQSYe5yGjZt2mR16tSxjRs3Wu3atbN9OKGVrDg7vlt2sl5KH9zco1RZnFQ7bvt9LAAg/NdvMknIGj89iBLJ5DBXssLx0j4WABB+1CQh8Avd1qtROa3FbgEAKA0yScgav0XXe+JGhDVCvOc/hd4MfQEAygpBErLGb9H1xu0/Fbq/etNOu+qFOYW2lbYLNwAA8RhuQ9aU1IMoFV4XbhWCAwCQCQRJyJriehClKtlis+kujgsAAMNtyKpkPYjq1qhsP27bndJrxS82WxaL4wIA8gd9ktJEn6TMiu9BpMLsAc98lNZrPdKvvVWtVCFp/yVhdhwA5KdN9ElC2MT3IFLQpKyPnxYB8RrWrGo3TppXJovjAgDyBzVJCHy9kl/eYrP6pqwWxwUA5A+CJASWhsOu69Xa9+MV/CiwWrtlZ5ktjgsAyB8ESQi0QT1aW+Pa/vopqTO3htD89l9Kd3FcAEB+IEhC4Ifd7jrD37Dbhm273RBaSf2XvGE5PQ4AgGQIkhCKYbdLjm3uewituP5LmVwcFwCQ2wiSEAoaRktlCM3rv6TFcGOxOC4AwC9aACAUvCE0LT+SaGp/wX8CoNghNAVCCq5i+y+xEC4AwC+CJISiuaSCGw2RqUGkQpyIzyG0+P5LAAD4RZCEwCluOZFES5gog8RSIwCATGNZkjSxLEnZBUglLSfCEBoAIF0sS4IyH/oqi6BEP0dZIj/LiTCEBgAoawRJSHvoK9PDWwrE/C4nQpAEAChrtABAiUNf8YGLZphpu/anmzGatWydTZm70n3V/VSWCWE5EQBAeSCThFIPfaUy9FZcZorlRAAAQUImCaUe+spUZmrD1l0sJwIACAyCJCSU6aGvkjJTcvdri2zoqW3c9ywnAgDItkAGSVOmTLGWLVtapUqVrH379vbFF1+47QsXLrROnTpZvXr17KabbrLY7gUzZsywNm3aWMOGDW3EiBGFXm/SpEnWrFkz23fffW3ChAmF9o0aNcoaNWrkft706dPL6R0GX6aHvvxmpurVrMpyIgCAQAhckLRs2TK7+OKL7b777rOVK1faQQcdZAMHDrSdO3fa6aefbh07drRPP/3UFi1aZM8995x7zpo1a+yMM86w/v3726xZs+z555+3d955JxpYDRgwwIYOHWpTp061O+64w5YsWeL26f6NN95oTz31lI0fP979nHXr1mX1/QdtGZBMDX2lkplSIPTBzT1swmVd7JF+7d1X3SdAAgDkdZCkrJECpHPOOcdleK688kqbM2eOvf76667xk7JErVq1snvvvdeeeeYZ9xwFRcoSKRBq3bq1C4S8fWPGjLHu3bu7AKhdu3Y2aNAgGzdunNs3evRou/DCC613797WtWtX93Xy5MlZff9BoSEtDW1laugr1cyUt5xI7/ZN3VeG2AAAlu9B0mmnnWa/+93voveV9VHgM2/ePOvSpYvVqFHDbT/88MNdNkm0T4FQQcEvF9LOnTvb7Nmzo/t69OgRfT2/++Ipk6UunbG3XKfMTaaGvjKdmQIAIK9bAOzatcseeughu/766+3LL7+0Fi1aRPcpIKpYsaJt2LDBBSxt2/6S9RC1GV+1apX7Xvtin+d3X7zhw4fbsGHDLN8oEMrEMiBeZirVBWoBAMiWwGWSYt15551Ws2ZNN1SmIu6qVasW2l+tWjXbtm1bkX3edkl3X7whQ4a44T7v9s0331i+yNTQVyYzUwAA5G0mSTPNNPPsww8/tMqVK1v9+vVdEXaszZs3W5UqVdw+FW/Hb5d098VTMBUfpCF7mSkAAPIyk7R8+XI3U01BkjeMpqn/mrkW+xjVCSnQid+nQu+mTZsmfJ7ffSg7FGUDAMIgcEHS9u3bXfG2Zpr16dPHtmzZ4m7HH3+8qyEaO3ase5xmt/Xq1cvVJWn6/8yZM23atGm2e/due+CBB+zEE090j+vbt69NnDjRFixY4F7n0Ucfje47++yz7fHHH3etBr7//ns3I87bB/PdJHLmv9bag1MX24NTl9jML9dG12IDACDMCiKxHRkD0kjyzDPPLLJdmaP58+e7DFP16tWtQoUK9u6770YzTU888YQNHjzYatWqZXXr1nUZIrUQkNtuu80efPBBV3OkmXLvv/++ew299QsuuMD+9re/ucf17NnTXnnllegsueIoYKtTp46rT1LBdz7SMiO3vLTAfty2u9D2ujUq231ntaPGCAAQOKlcvwMXJJVk9erVbpq+2gE0aNCgSCC1ePFil3VSsBRL7QKUMerWrVuRuqNPPvnEtm7d6vb5CZDCHCQpy5OJeiAFSFeM/6zYxzxBMTYAIGByOkgKijAGSQpstH5a7PIgdatXtouPbWGDehzoO1hSoHXsfW/b6k07i32c+h6pUzY1RwCAMF6/A1eThLILkNSjKH79tB+377aR05Zax3veco/xQ5mokgIk0c/SYwEACCOCpDygzI8ySMWlDFVXpOEzP4GS33XYUn0sAABBQpCUB5TNic8gJaNgqqTZaX7XYUv1sQAABAlBUh5IJZvjZ4hMxd6Na5fcWJO12AAAYUaQlAdSzeaUFFSpEPuuMw4t8XVYiw0AEGYESXlA2RxldTIZVGlqv6b4qydSvHo1KjP9HwAQeoFduw2Zo2yOsjqa3VZctVHBfxab9TtE5q3D9uGydTbr32vdK2iZkS4tWWoEABB+BEl5QgHN6PM7JOyQLQVpDpHpsce2buhuAADkEobb8ixQmn37CXZdr9auiWQsZZAURDFEBgDAL+i4nUcdt8tieRIAAHL1+s1wW55SQKT6IQAAkBhBUgCR5QEAIPsIkkKwCK2m76ugmnohAADKD4XbIViEdvXGHW673wVoAQBA6REkhWAR2kgK66oBAIDMIEgKySK0EZ/rqgEAgMwgSArZIrSpLFYLAADSR5AUskVoV6zdVubHAgAACJICtwhtSRP9H562NFrArfqkWcvW2ZS5K91X6pUAAMgcWgAEbBHaK8Z/VuJjVcC9Z0/E7n7tC1oFAABQRsgkBUzNqhWL3e8VcF/1whxaBQAAUIYIkgLWI2nrzp/Tfg1aBQAAkDkESQHvkZQqWgUAAJAZBEkh6JGUDloFAABQOgRJAVAWAY3flgIAACAxgqQA8BvQNKhZxR4/r0OxrQK0XfvVUgAAAKSPFgAB6pGk2WnJ6pLq16xss4b0tCqVKliFCuaKvBUQxT7eC5zUBkAtBQAAQPrIJAWoR5LEhzYF/7nd26edC5BEfZBGn9/BGtcpnIHSfW2nTxIAAKVXEIlEmCuehk2bNlmdOnVs48aNVrt27Yy1AdAsN78NIjUrTkXfqmnSkJ0yUmSQAADIzPWbIClAQZIQ+AAAEIzrNzVJAaOA6JhWDbJ9GAAA5D1qkgAAABIgSAIAAEiAIAkAACABgiQAAIAECJIAAAASIEgCAABIgCAJAAAgAYIkAACABAiSAAAAEqDjdpq81VzU3hwAAISDd932syobQVKaNm/e7L7uv//+2T4UAACQxnVca7gVhwVu07Rnzx5btWqV7bXXXlZQUJATkbUCvm+++SajC/bmIs6Vf5wrfzhP/nGu/ONcJaawRwHSvvvuaxUqFF91RCYpTTqx++23n+Ua/UPiH5M/nCv/OFf+cJ7841z5x7kqqqQMkofCbQAAgAQIkgAAABIgSIJTtWpVu/POO91XFI9z5R/nyh/Ok3+cK/84V6VH4TYAAEACZJIAAAASIEgCAABIgCAJAAAgAYIkAAAC7Mcff7SPPvrINmzYkO1DyTsESTls8ODBrhu4dzvwwAPd9oULF1qnTp2sXr16dtNNNxVav2bGjBnWpk0ba9iwoY0YMaLQ602aNMmaNWvmupROmDDBwm7t2rXWokULW7FiRXRbWZybUaNGWaNGjaxly5Y2ffp0y5Vzlezzlc+fsSlTprjfc6VKlax9+/b2xRdfuO18rvydJz5TRb344ovWvHlzGzhwoGtgrPvCZ6qcaHYbctMxxxwTee211yIbNmxwt02bNkV27NgRad68eeTyyy+PfPnll5FTTjkl8uyzz7rH//DDD5HatWtHhg0bFlm6dGmkQ4cOkenTp7t9CxYsiFSpUiXy9NNPR+bPnx858MADI4sXL46E1Zo1ayJHH320/q8SWb58udtWFufmjTfeiFSrVi3y8ssvR2bOnBlp0aJFZO3atZGwn6tkny/J18+Y3mu9evUif/nLXyKrV6+O/Nd//Veka9eufK58nifhM1XYjz/+GGnYsGFk3rx57v7YsWMjzZo14zNVjgiSctTu3bvdP5TNmzcX2j558mT3P6itW7e6+3Pnzo0ce+yx7vuRI0dGDjnkkMiePXvcff1jGTBggPv+mmuuiZx44onR13n44Ycjt912WySsevbsGXnkkUcKXfjL4tz07t3b/Y/Mc+2117r/QYX9XCX7fOXzZ+zVV1+NPPnkk9H7ujBVr16dz5XP88Rnqqivv/46Mn78+Oh9BUu1atXiM1WOGG7LUQsWLHCL8CqVXb16dTvppJPs66+/tnnz5lmXLl2sRo0a7nGHH364LVq0yH2vfd27d48u2Nu5c2ebPXt2dF+PHj2irx+7L4yefvppl9qPVRbnJhfOW6JzlezzJfn6GTvttNPsd7/7XfT+kiVLrHXr1nyufJ4nPlNFaXHaAQMGuO93795tI0eOtD59+vCZKkcESTlK/2AOPvhgGzdunM2fP9+N/et/TFoVWrUlHv1DqlixoisIjN+nBRFXrVrlvi9uXxjFvhdPWZybXDhvic5Vss+X8Bkz27Vrlz300EN2xRVX8LnyeZ74TCWnAKZx48b2xhtv2KOPPspnqhxVKs8fhvKjvz68v0Dk8ccfd/8AVMwX36K+WrVqtm3bNvc/pdh93nYpbl+uiH+PmTg3uXrekn2+9D/asjiPYaOlIGrWrOmKbW+//XY+Vz7OU+XKlflMJaFM0ZtvvmnXXXedO1etWrXiM1VOyCTliX322celsvXXyJo1awrt27x5s1WpUsXq169faJ+3XYrblyvi32Mmzk0+nLfYz9d3331XJucxTDQrSLOEXnjhBXfh53Pl7zzF4zNlhTJFHTt2tD//+c/20ksv8ZkqRwRJOUpTQvU/H8+sWbOsQoUK1q5dO/e9Z/ny5bZz5073D0TTSWP3zZkzx5o2beq+L25froh/j5k4N7l63pJ9vlRDURbnMSz0Xvv37+8u/m3btnXb+Fz5O098porSVH6dF4+CFgVMGhHgM1VOyrNKHOVn3LhxbgrntGnTIlOnTo0cdNBBkYsuusjNINl7772j00UHDhwYOe2006JTvTUF9K233ors2rUrctJJJ0UGDRoUnT1Rs2ZNN21Us0/at28fefDBByNhFz9jK9PnZsqUKZEmTZpEvv32WzfduWnTppFJkyZFwn6ukn2+JF8/Y9u2bYu0bds2ctlll7nj9256n3yuSj5P//u//8tnKs6qVavcjD/NBtRMtwsuuMC9P/5fVX4IknLYLbfcEqlTp06kfv36kcGDB0e2bNkS/cdQo0aNSIMGDdw/tM8//zz6nNGjR0cqV67sppfqf1j6x+K59dZbXY8N/aPt2LGj+59d2MX3/sn0udE03PPPP99NcdZN/yPzpuaG/Vwl+3zl62dMU611juJvOmd8rvydJz5TRb355psuqNxrr70iZ599tuuDJHymykeB/lNeWSsEx+rVq930Tk0jbdCgQaF9St0uXrzYjj/+eKtVq1ahfZqBsnLlSuvWrVvOjleXxbn55JNPbOvWrW6fNzU31/EZK4zPVenxmSqMz1TZI0gCAABIgMJtAACABAiSAAAAEiBIAgAASIAgCQAAIAGCJAChpxk57733Xpn+jJ9//rlMXx9A8BAkAQi9f/7zn3bppZfaTz/9VOzjhg8fbt9//737/tZbb7V+/fqVGBh5r/nyyy/bcccd574fMWKE3XLLLUmf9+2337pu0c2bN4/edH+//faL3tcCorHdlAEEDwvcAggdLcEQ273k3HPPtb///e/2zTffWJMmTaIBzu7du61u3bru/ltvvWWjR4+2q6++2t1Xfxgt6hlLj49dR+zFF1+0O+64w23TYp9a2+qwww6ztWvXunXF9DP19YsvvrAtW7a4xVpFC4XWqFHDVqxYEX0t9ap599137cADD3T3Bw0aVGSRUgDBQpAEIHS0OvyGDRusYsWKhbZrbUKPgiQFU7t27XJN8W677TYX0GhFdfnxxx9dlkhZHc8hhxxib7zxRvS+Mk1etkkBzj333GPTpk2z++67z3bs2GF33XWXe5169eoVasqnrJEf8UEagGDhXyiA0FGWZuzYsXbiiSf6evyTTz7pgpmvvvoqGlgpwPnyyy9t/PjxSZ+3ZMkSlzk6+OCDbePGje41YjNJkyZNco879NBDXcDlZaH0M7Zv3+4e69H9U045JRpMaXX73//+96U6DwDKFkESgNCpXr26GxrzQ8HLBRdcYCeffHKRzFMiyjx5gcyqVatsr732sosuusjef/99N3z229/+1t555x3383/zm98UGprTzxENBeoYFy5cWGi47R//+Eeh4TYAwUaQBCCUlMnxQwXX1157bdKhrdjhNlHws3TpUldf1KlTJxcc/elPf7JPP/3UJk6c6GqeVPytobwzzzzTDfspq/Xqq69GgyRmwgG5gSAJQCgDJL+Lb5599tnulqqPPvrIxowZY6+99pobGjvggAPsyiuvdPu84bbp06fb3nvv7YbSBg8eHH2u6pVU6B0bgOn+r3/962iwtn79erv++utTPi4A5YcgCUDoaCaZ6pJSceONN9pjjz1WZEV0jzJCmq2mYTnZZ599XEAzbtw4u/zyy139ksycOdNatmzpMkoq5tawWeywmhdE7b///iXObgMQbPRJAhAqqhlSZqdp06YpPU91RpqppgAm0U1BT7Vq1QrNoFPLgGbNmkW3KXuk11B2KXZbvAULFkRn0QEIL4IkAKHywQcfuICnVatWKT3P77T8eKo98vz5z392Q2kavnv00Udd/ZKCqUR1UCeccEKhbSrmju3tpLolv0OGALKDIAlAqNx777126qmnFmr66IcyPi+88IJrLpnopplsylLFU13Rww8/bLfffrtdddVVroh75cqVrl5Jhdva/+9//7tQ24DXX3/dNbiMn2Xnvf4VV1zhZsOptQCA4KImCUBozJgxw02/V1F1qpT1Oe+88+y5555LuF9LhsS3FbjwwgtdwLNp0ybr1auX+/mdO3d2++bPn29vv/22DRs2zLp27epqllR3tHXrVrv77rutUaNGhV5LAZp+hhxzzDF2+umnu2APQHAVRGLzvwAQcJqef9BBB2X8dRUIqRg8tlXA559/7gq6jzjiCNcvKRm1BIgPigCEH0ESAABAAtQkAQAAJECQBAAAkABBEgAAQAIESQAAAAkQJAEAACRAkAQAAJAAQRIAAEACBEkAAABW1P8BDQhp8dcgFpIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "plt.scatter(x, y)\n",
    "\n",
    "plt.title('广告费用和销售收入之间的关系')\n",
    "plt.xlabel('广告费用')\n",
    "plt.ylabel('销售收入')\n",
    "#plt.savefig('images/5-16.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa8f1bab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b8cbab3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "241f3459",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
